Chinese Named Entity Recognition On Weibo Ner

Metrics

F1

Results

Performance results of various models on this benchmark

		Paper Title
BERT-CRF (Replicated in AdaSeq)	72.77	Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning
Baseline + BS	72.66	Boundary Smoothing for Named Entity Recognition
FGN	71.25	FGN: Fusion Glyph Network for Chinese Named Entity Recognition
BERT-BiLSTM-context	70.98	Supplementary Features of BiLSTM for Enhanced Sequence Labeling
SA-NER	69.8	Named Entity Recognition for Social Media Texts with Semantic Augmentation
AESINER	69.78	Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information
Locate and Label	69.16	Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
FLAT+BERT	68.55	FLAT: Chinese NER Using Flat-Lattice Transformer
Glyce + BERT	67.6	Glyce: Glyph-vectors for Chinese Character Representations
SLK-NER	64	SLK-NER: Exploiting Second-order Lexicon Knowledge for Chinese NER
Collaborative Graph Network	63.09	Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
NFLAT	61.94	NFLAT: Non-Flat-Lattice Transformer for Chinese Named Entity Recognition
LSTM + Lexicon augment	61.24	Simplify the Usage of Lexicon in Chinese NER
FLAT	60.32	FLAT: Chinese NER Using Flat-Lattice Transformer
Lattice	58.79	Chinese NER Using Lattice LSTM
TENER	58.17	TENER: Adapting Transformer Encoder for Named Entity Recognition
BiLSTM+CRF+adversarial+self-attention	53.08	Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention Mechanism
CAN-NER Model	-	CAN-NER: Convolutional Attention Network for Chinese Named Entity Recognition

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